Method and system for data-driven and modular decision making and trajectory generation of an autonomous agent

ABSTRACT

A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/333,518, filed 28 May 2021, which is a continuation of U.S.application Ser. No. 17/125,668, filed 17 Dec. 2020, which claims thebenefit of U.S. Provisional Application No. 63/035,401, filed 5 Jun.2020, and U.S. Provisional Application No. 63/055,763, filed 23 Jul.2020, each of which is incorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the autonomous vehicle field, andmore specifically to a new and useful system and method for data-driven,modular decision making and trajectory generation in the autonomousvehicle field.

BACKGROUND

Making safe and effective decisions in an autonomous vehicle is acomplex and difficult task. This type of decision-making requiresunderstanding of the current environment around the vehicle, anunderstanding of how this environment will evolve in the future, alongwith other factors involved in achieving safe and continuous progresstowards the predefined driving goal. All decisions have to becontinuously constrained by both driving rules of the road and humandriving conventions, which is a difficult problem even for humans attimes, and therefore an exceptionally challenging problem to implementwith autonomous vehicles. Both the complicated nature of the drivinginteractions and the immense number of possible interactions makesdecision-making and trajectory generation a tremendously difficultproblem for autonomous systems. Regardless of the complexity, autonomousvehicles are tasked with solving this problem continuously; thus, afeasible solution which ensures scalability along with the safety of allroad users is essential.

Conventional systems and methods have approached this problem in one oftwo ways —programmed or learned. Programmed motion planners produce aset of rules and constraints hand tuned and optimized by experts.Examples of this include conventional decision tree architecturesemploying data-driven models, which have only been utilized inrestricted capacities such as perception. Conventional programmedapproaches suffer from numerous limitations, such as, but not limitedto: the production of unnatural decisions and motions (e.g., as shown inthe programmed trajectory in FIG. 5); an exhaustive list of scenarios toprogram; and others. In contrast, learned motion planners involveanalyzing large amounts of human driving data and/or running drivingsimulations. Examples of this include holistic end-to-end systems andsingle monolithic networks to address an entire driving policy module(e.g., mid-to-mid systems). Learned approaches also suffer from numerouslimitations and drawbacks, such as, but not limited to: lack of safetyassurances (e.g., as a result of treating the problem of motion planningin an end-to-end fashion, traditional learning algorithms are not ableto provide safety assurances regarding the trajectories created by theirnetworks); sample sparsity (e.g., the ability to capture all possiblesamples [driving scenarios] that the vehicle will be encountering in thereal world); a lack of interpretability and/or explainability; andothers.

Thus, there is a need in the autonomous vehicle field to create animproved and useful system and method for decision making and trajectorygeneration.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a system for modular decision making andtrajectory generation.

FIG. 2 is a schematic of a method for modular decision making andtrajectory generation.

FIGS. 3A-3C depict a variation of a system for modular decision making,a variation of a deep decision network (set of 1st learning modules),and a variation of a deep trajectory network, respectively.

FIG. 4 depicts a variation of a deep decision network (set of 1stlearning modules).

FIG. 5 depicts a naturalistic trajectory versus a programmed trajectory.

FIG. 6 depicts a variation of a high-level architecture of a planningmodule of the system 100.

FIG. 7 depicts a schematic variation of an overall system of theautonomous agent.

FIG. 8 depicts a schematic variation of context-aware decision makingand trajectory planning.

FIGS. 9A-9B depict a variation of a use case of an autonomous vehicle infixed-route deliveries and a schematic of fixed routes driven by thevehicles.

FIG. 10 depicts a variation of a set of contexts.

FIG. 11 is a schematic of a variation of the method 200.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1, a system 100 for data-driven, modular decisionmaking and trajectory generation includes a computing system.Additionally or alternatively, the system can include and/or interfacewith any or all of: an autonomous agent (equivalently referred to hereinas an autonomous vehicle and/or an ego vehicle); any number of modulesof the autonomous agent (e.g., perception module, localization module,planning module, etc.); a vehicle control system; a sensor system;and/or any other suitable components or combination of components.

Additionally or alternatively, the system 100 can include and/orinterface with any or all of the systems, components, embodiments,and/or examples described in U.S. application Ser. No. 17/116,810, filed9 Dec. 2020, which is incorporated herein in its entirety by thisreference.

As shown in FIG. 2, a method 200 for data-driven, modular decisionmaking and trajectory generation includes: receiving a set of inputsS205; selecting a learning module (equivalently referred to herein as alearned model, a trained model, and a machine learning model, a micromodule, and/or any other suitable term) from a set of learning modulesS210; producing an output based on the learning module S220; repeatingany or all of the above processes S230; and/or any other suitableprocesses. Additionally or alternatively, the method 200 can includetraining any or all of the learning modules; validating one or moreoutputs; and/or any other suitable processes and/or combination ofprocesses.

Additionally or alternatively, the method 200 can include and/orinterface with any or all of the methods, processes, embodiments, and/orexamples described in U.S. application Ser. No. 17/116,810, filed 9 Dec.2020, which is incorporated herein in its entirety by this reference.

In preferred variations of the method 200 as shown in FIG. 11, themethod for data-driven, modular decision making and trajectorygeneration includes: receiving a set of inputs S205; as part of S210,selecting a 1^(st) learning module (equivalently referred to herein as adeep decision network) S212; as part of S220, defining an action spaceand/or selecting an action S222; as part of S210, selecting a 2^(nd)learning module (equivalently referred to herein as a deep trajectorynetwork) based on the action S214; as part of S220, generating a vehicletrajectory based on the 2^(nd) learning module S224; and validating thevehicle trajectory S260. Additionally or alternatively, the method 200can include any or all of: receiving and/or determining a vehiclecontext S205; determining a latent space representation S222; repeatingany or all of the above processes S230; and/or any other suitableprocesses. Additionally or alternatively, the method 200 can include anyother suitable processes.

The method 200 is preferably performed with a system 100 as describedabove, but can additionally or alternatively be performed with any othersuitable system(s) for autonomous driving, semi-autonomous driving,and/or any other autonomous or partially autonomous system(s).

2. Benefits

The system and method for data-driven, modular decision making andtrajectory generation can confer several benefits over current systemsand methods.

In a first set of variations, the system and/or method confer thebenefit of capturing the flexibility of machine learning (e.g., deeplearning) approaches while ensuring safety and maintaining a level ofinterpretability and/or explainability. In specific examples, the systemestablishes and the method implements a hybrid architecture, whichrefers to an architecture including both programmed and learned portions(e.g., processes), which can have numerous advantages over and/orminimize the limitations of either of the individual approaches. Inspecific examples of the system and/or method, this approach and itsadvantages are enabled through a limited ODD and fixed route framework.

In a second set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit of reducingan amount of data required to train each of a set of learning modules(e.g., 1^(st) and 2^(nd) learning modules). In specific examples alimited ODD and fixed route architecture enables the system and/ormethod to overfit the learning modules for fixed routes, which cansubsequently enable any or all of: faster learning due to the reducedmodel scale and complexity of any or all of the learning modules; a needfor exponentially less data to build a safe urban autonomy stack; avalidation of the learning modules leading to guaranteed safety; ascalability of the system and/or method (e.g., for adding new routes); aminimizing and/or elimination of edge cases; and/or any other suitablebenefits or outcomes.

In a third set of variations, additional or alternative to thosedescribed above, the system and/or method confers the benefit ofutilizing an awareness of the vehicle's context to hypertune lossfunctions of the learning modules to these particular contexts whentraining them. This can subsequently function to increase an accuracyand confidence in scenario-specific events. In specific examples,training each of a set of decision making learning modules (1^(st) setof learning modules) includes hypertuning a loss function to aparticular context associated with the learning module in a 1:1 mapping.

In a fourth set of variations, additional or alternative to thosedescribed above, the system and/or method confers the benefit ofmaintaining explainability while generating naturalistic trajectoriesfor the agent which accurately mirror human driving through theprogrammed selection of modular learning modules at the decision-makingstage (1^(st) set of learning modules) and at the trajectory generationstage (2^(nd) set of learning modules).

In a fifth set of variations, additional or alternative to thosedescribed above, the system and/or method confers the benefit ofenabling a data-driven approach to the modular decision making andtrajectory generation.

In a sixth set of variations, additional or alternative to thosedescribed above, the system and/or method confers the benefit ofimproving the operation of one or more computing systems involved indecision making and trajectory generation, which can be enabled, forinstance, through any or all of: the organization of the computingprocess and/or system into a modular architecture of smaller learningmodules; reducing the information processed in trajectory generation bylocalizing the environment of the vehicle based on a selected action;hypertuning each of a 1^(st) set of micro learning modules to aparticular context of the vehicle; hypertuning each of a 2^(nd) set ofmicro learning modules to a particular action of the vehicle; creating acentralized and parallel computing model which enables a highconcurrency of task execution, low latency, and high throughput; and/orthrough creating any other suitable framework.

Additionally or alternatively, the system and method can confer anyother benefit(s).

3. System

As shown in FIG. 1, the system 100 for data-driven, modular decisionmaking and trajectory generation includes a computing system.Additionally or alternatively, the system can include and/or interfacewith any or all of: an autonomous agent (equivalently referred to hereinas an autonomous vehicle and/or an ego vehicle); any number of modulesof the autonomous agent (e.g., perception module, localization module,planning module, etc.); a vehicle control system; a sensor system;and/or any other suitable components or combination of components.

The system 100 functions to enable modular decision making andtrajectory generation of an autonomous agent and includes: a computingsystem, wherein the computing system can include and/or implement a setof learning modules (e.g., 1^(st) set of learning modules, 2^(nd) set oflearning modules, etc.) and optionally a trajectory generator, atrajectory validator, and/or any other suitable components and/ormodules. Additionally or alternatively, the system can include and/orinterface with any or all of: a localization module; a predictionmodule; a perception module; the autonomous agent (equivalently referredto herein as an autonomous vehicle and/or an ego vehicle); a vehiclecontrol system; a sensor system; and/or any other suitable components orcombination of components.

The system 100 is preferably configured to implement and/or interfacewith a hybrid architecture of decision making and/or trajectorygeneration (e.g., as shown in FIG. 6, as shown in FIG. 7, as shown inFIG. 8, as shown in FIG. 3A, etc.), the hybrid architecture implementingboth classical, rule-based approaches and machine learning approaches.This is preferably enabled by a constrained and/or structured ODD (e.g.,well-defined, specified, etc.) and fixed route driving framework (e.g.,a non-geofenced driving framework), which functions to maintainexplainability of the vehicle's decision making while enabling thevehicle to drive with human-like driving behavior on routes validatedwith minimal training data. Additionally or alternatively, the system100 can be any or all of: configured to implement and/or interface withany suitable architecture configured to produce any suitable outputs atany part of autonomous vehicle operation (e.g., in planning, in motionplanning, in trajectory planning, in perception, in localization, etc.);the autonomous agent can interface with any other driving framework(e.g., large ODD, non-fixed routes, geofenced, etc.); and/or the system100 can be otherwise suitably configured.

In preferred variations, for instance, the system 100 defines a modular,hybrid architecture which is configured to implement both programmed andlearned processes of the method 200. The system preferably functions toachieve the safety assurances and explainability/interpretability fromprogrammed processes while maintaining the naturalistic and adaptiveprinciples of learning processes. In preferred variations, the system100 achieves this using a hybrid architecture which decomposes the taskof motion planning and combines sets of micro-learning algorithms (whichform and/or are integrated within the set of learning modules)sandwiched between a set of programmed safety constraints, wherein eachof the learning modules' intended functionality is restricted tospecific, explainable (and thus verifiable) tasks (e.g., based on acontext and/or other environmental features of the vehicle). The system100 can optionally implement and/or interface with (e.g., integratewith) a set of rule-based fallback and validation systems which arebuilt around these learning modules to guarantee target safety and toensure the safety of the resulting trajectory. With this architecture, avalidation of the performance and underlying properties of each of theselearning modules can be achieved, thereby enabling a much safer and moreeffective system to be built.

In specific examples (e.g., as shown in FIGS. 9A-9B), the system 100 isimplemented in autonomous short-haul (e.g., between 5 and 400 miles) B2Bfixed-route applications. In these variations, the autonomous agentspreferably receive inventory from sorting centers, but can additionallyor alternatively receive inventory for parcel hubs and/or warehouses.The agent then preferably delivers the inventory to and/or between anyor all of: sorting centers, micro-fulfillment centers, distributioncenters, retail stores, and local delivery centers. Additionally oralternatively, the agents can interface with residences (e.g., customerhomes), and/or any other suitable locations/facilities.

Additionally or alternatively, the system 100 can be implemented in anyother suitable way(s).

3.1 System—Components

The system 100 includes a computing system, which functions to enablemodular decision making (e.g., motion planning) and/or trajectorygeneration of an autonomous agent. Additionally or alternatively, thecomputing system can function to perform any or all of: route planningof the vehicle at a planning module; validating a trajectory of thevehicle; localization of the vehicle and/or surrounding objects at alocalization module; path prediction of the vehicle and/or objectssurrounding the vehicle at a prediction module; storage of information;and/or any other suitable functions.

The computing system is preferably configured to implement centralizedand parallel computing which enables any or all of: high concurrency oftask execution, low latency, high data throughput, and/or any othersuitable benefits. Additionally or alternatively, the computing systemcan be configured to perform any other computing and/or processing(e.g., decentralized computing, distributed computing, serial computing,etc.) and/or can confer any other suitable benefits.

Additionally or alternatively, the system and/or computing system can beotherwise configured and/or designed.

The computing system is preferably arranged at least partially onboard(e.g., integrated within) the autonomous agent.

In preferred variations, the autonomous agent includes an autonomousvehicle that is preferably a fully autonomous vehicle and/or able to beoperated as a fully autonomous vehicle, but can additionally oralternatively be any semi-autonomous or fully autonomous vehicle, ateleoperated vehicle, and/or any other suitable vehicle. The autonomousvehicle is preferably an automobile (e.g., car, driverless car, bus,shuttle, taxi, ride-share vehicle, truck, semi-truck, etc.).Additionally or alternatively, the autonomous vehicle can include any orall of: a watercraft (e.g., boat, water taxi, etc.), aerial vehicle(e.g., plane, helicopter, drone, etc.), terrestrial vehicle (e.g.,2-wheeled vehicle, bike, motorcycle, scooter, etc.), and/or any othersuitable vehicle and/or transportation device, autonomous machine,autonomous device, autonomous robot, and/or any other suitable device.

The computing system can additionally or alternatively be arrangedremote from the autonomous agent, such as a cloud computing system. Theremote computing system is preferably in communication with the onboardcomputing system (e.g., to collect information from the onboardcomputing system, to provide updated models to the onboard computingsystem, etc.), but can additionally or alternatively be in communicationwith any other suitable components.

The computing system preferably includes active and redundantsubsystems, but can additionally or alternatively include any othersuitable subsystems.

The computing system preferably includes a planning module of thecomputing system, which further preferably includes any or all of: a setof learning modules (e.g., deep learning models); a trajectorygenerator; a trajectory validator; and/or any other suitable components.The set of learning modules preferably includes a set of deep decisionnetworks (neural networks) which function to determine an action of theagent (based on context) and a set of deep trajectory networks (neuralnetworks) which function to determine a trajectory for the agent (basedon the action).

The computing system further preferably includes a processing system,which functions to process the inputs received at the computing system.The processing system preferably includes a set of central processingunits (CPUs) and a set of graphical processing units (GPUs), but canadditionally or alternatively include any other components orcombination of components (e.g., processors, microprocessors,system-on-a-chip (SoC) components, etc.).

The computing system can optionally further include any or all of:memory, storage, and/or any other suitable components.

The computing system is further preferably configured to (e.g., able to,organized to, etc.) perform the computing associated with one or moremodular sets of learning modules (equivalently referred to herein aslearning agents or learning models), wherein each learning moduleincludes a set of one or algorithms and/or models configured to producea set of one or more outputs based a set of one or more inputs.

A single computing system can be used to do the computing for all ofthese modules, separate computing systems can be used (e.g., with anindividual computing system for each learning module), and/or anycombination of computing systems can be used.

The computing system can optionally include middleware framework, whichextracts dependencies from modules and links them all together (e.g.,with a topological ordering process such as a directed acylic graph,etc.).

In addition to the planning module, the computing system can includeand/or interface with any or all of: a localization module, predictionmodule, perception module, and/or any other suitable modules foroperation of the autonomous agent.

The computing system (e.g., onboard computing system) is preferably incommunication with (e.g., in wireless communication with, in wiredcommunication with, coupled to, physically coupled to, electricallycoupled to, etc.) a vehicle control system, which functions to executecommands determined by the computing system.

The computing system can include and/or interface with a map, whichfunctions to at least partially enable the determination of a contextassociated with the autonomous agent. The map is preferably a highdefinition, hand-labeled map as described below, which prescribes thecontext of the autonomous agent based on its location and/or positionwithin the map, but can additionally or alternatively include any othermap (e.g., automatically generated map) and/or combination of maps.

The system 100 preferably includes and/or interfaces with a sensorsystem, which functions to enable any or all of: a localization of theautonomous agent (e.g., within a map), a detection of surroundingobjects (e.g., dynamic objects, static objects, etc.) of the autonomousagent, and/or any other suitable function.

The sensor system can include any or all of: cameras (e.g., 360-degreecoverage cameras, ultra-high resolution cameras, etc.), light detectionand ranging (LiDAR) sensors, radio detection and ranging (RADAR)sensors, motion sensors (e.g., accelerometers, gyroscopes, inertialmeasurement units [IMUs], speedometers, etc.), location sensors (e.g.,Global Navigation Satellite System [GNSS] sensors, Inertial NavigationSystem [INS] sensors, Global Positioning System [GPS] sensors, anycombination, etc.), ultrasonic sensors, and/or any suitable sensors.

In a set of variations, the sensor system includes: 16-beam LIDARs(e.g., for high fidelity obstacle detection, etc.); short range RADARs(e.g., for blind spot detection, cross traffic alert, emergency braking,etc.); ultrasonic sensors (e.g., for park assist, collision avoidance,etc.); 360-degree coverage cameras (e.g., for surround view forpedestrian/cyclist/urban obstacle detection and avoidance, etc.);128-beam LIDAR (e.g., for localization of vehicle with high precision);long range ultra-high resolution cameras (e.g., for traffic sign andtraffic light detection); long range RADARs (e.g., for long rangeobstacle tracking and avoidance); GNSS/INS (e.g., for ultra highprecision localization); and/or any other suitable sensors.

In a first variation of the system 100, the system includes a computingsystem which includes the agent's planning module and includes and/orinterfaces with the agent's perception and/or localization module(s),which includes the vehicle's sensor system(s).

Additionally or alternatively, the system 100 can include any othersuitable components or combination of components.

4. Method

As shown in FIG. 2, a method 200 for data-driven, modular decisionmaking and trajectory generation includes: receiving a set of inputsS205; selecting a learning module (equivalently referred to herein as alearned model, a trained model, and a machine learning model, a micromodule, and/or any other suitable term) from a set of learning modulesS210; producing an output based on the learning module S220; repeatingany or all of the above processes S230; and/or any other suitableprocesses. Additionally or alternatively, the method 200 can includetraining any or all of the learning modules; validating one or moreoutputs; and/or any other suitable processes and/or combination ofprocesses.

In preferred variations of the method 200 as shown in FIG. 11, themethod for data-driven, modular decision making and trajectorygeneration includes: receiving a set of inputs S205; as part of S210,selecting a 1^(st) learning module S212; as part of S220, defining anaction space and/or selecting an action S222; as part of S210, selectinga 2^(nd) learning module based on the action S214; as part of S220,generating a vehicle trajectory based on the 2^(nd) learning moduleS224; and validating the vehicle trajectory S260. Additionally oralternatively, the method 200 can include any or all of: receivingand/or determining a vehicle context S205; determining a latent spacerepresentation S222; and/or any other suitable processes. Additionallyor alternatively, the method 200 can include any other suitableprocesses.

The method 200 preferably functions to perform decision making andtrajectory generation of an autonomous agent, further preferably basedon a context of the vehicle. Additionally or alternatively, the method200 can function to perform only decision making, perform onlytrajectory generation, perform any part or process of vehicle planning(e.g., motion planning, path planning, maneuver planning, etc.), performany other part or process of autonomous vehicle operation (e.g.,perception, localization, etc.), select an action for the vehicle froman action space, validate a vehicle trajectory and/or any other output,and/or can perform any other suitable function(s).

The method 200 further preferably functions to perform decision makingand trajectory generation (and/or any other suitable processes) with amodular framework of learning modules (e.g., 1^(st) set of learningmodules, 2^(nd) set of learning modules, etc), wherein each of thelearning modules is configured to process inputs associated withparticular (e.g., predefined, predetermined, etc.) information (e.g., aparticular vehicle context for the 1^(st) learning modules, a particularvehicle action for the 2^(nd) learning modules, etc.).

The method 200 further preferably functions to utilize programmedprocesses (e.g., selection of 1^(st) learning modules based on context,selection of 2^(nd) learning modules based on action, trajectoryvalidation, etc.) along with the learned processes (e.g., machinelearning models, deep learning models, neural networks, etc.)implemented by the learning modules, which functions to maintain anexplainability and/or interpretability (e.g., relative to an end-to-endsystem, relative to a mid-to-mid system, etc.) of the outputs (e.g.,actions, trajectories, etc.).

Additionally or alternatively, the method 200 can function to performany or all of these processes independently of a context of the vehicle,in light of other information associated with the autonomous agent(e.g., historical information, dynamic information, vehicle state,etc.), within any other suitable framework, and/or the method 200 can beperformed in any other suitable way(s) to perform any suitablefunction(s).

Additionally or alternatively, the method 200 can perform any othersuitable function(s).

The method 200 is preferably performed throughout the operation of theautonomous agent, such as throughout the duration of the agent'straversal (e.g., according to a map which assigns a set of contexts) ofa route (e.g., fixed route, dynamically determined route, etc.), but canadditionally or alternatively be performed at any or all of: apredetermined frequency (e.g., constant frequency), in response to atrigger, at a set of intervals (e.g., random intervals), once, and/or atany other suitable times.

The method 200 is preferably performed with a system 100 as describedabove, further preferably with a computing system at least partiallyarranged onboard the autonomous agent, but can additionally oralternatively be performed with any suitable computing system and/orsystem.

4.1 Method—Receiving a Set of Inputs S205

The method 200 preferably includes receiving a set of inputs S205, whichfunctions to receive information with which to select one or morelearning modules (e.g., one of a 1^(st) set of learning modules, one ofa 2^(nd) set of learning modules, etc.). Additionally or alternatively,S205 can function to receive information which serves as an input to oneor more learning modules (e.g., input to a 1^(st) learning module, inputto a 2^(nd) learning module, etc.), receive information with which toperform other processes of the method (e.g., determining one or morelatent space representations, determining one or more environmentalrepresentations, etc.) and/or to trigger one or more processes, receiveinformation with which to otherwise operate the agent (e.g., duringperception, during localization, etc.), and/or can perform any othersuitable function(s).

S205 is preferably performed throughout the method 200, such as any orall of: continuously, at a predetermined frequency, at random intervals,prior to each of a set of processes of the method 200, and/or at anyother suitable times. S205 can additionally or alternatively beperformed in response to a trigger (e.g., based on a map, in response toa context being selected, based on sensor information, etc.), at a setof intervals (e.g., random intervals), and/or at any other suitabletime(s) during the method 200.

S205 is preferably performed with a system 100 as described above,further preferably with an onboard computing system and an onboardsensor system of the autonomous agent, but can additionally oralternatively be performed with any other components of the systemand/or any other suitable systems.

The set of inputs preferably includes information received from aperception module of the autonomous agent, such as the sensor system,and/or determined (e.g., calculated) based on sensors in the sensorsystem (e.g., via a perception module), but can additionally oralternatively be received from any suitable sources (e.g., internet,autonomous agent, historical information, remote computing system,etc.).

The set of inputs can include any or all of: a current state of theagent (e.g., position, heading, pitch, acceleration, deceleration,etc.); information associated with a set of dynamic objects (e.g.,current position, size, previous path, predicted path, etc.) such asthose proximal to the agent; information associated with a set of staticobjects (e.g., traffic cones, mailboxes, etc.) such as those proximal tothe agent (e.g., current state of static object, historical informationassociated with static object, etc.); a map and/or information from amap (e.g., HD map; hand-labeled map indicating a set of assignedcontexts; automatically-labeled map indicating a set of assignedcontexts; map indicating lane boundaries, connections between lanelines, positions of lanes, connectivity of lanes, semantic information,etc.; etc.); routing information required to reach a destination (e.g.,ideal path to take, sequence of lanes to take, etc.); one or moreuncertainty values and/or estimates (e.g., epistemic uncertainty,aleatoric uncertainty, etc.); autonomous agent state (equivalentlyreferred to herein as the ego vehicle state); and/or any other suitableinputs.

In one set of variations, for instance, the set of inputs includes ahigh definition, labeled (e.g., hand-labeled, automatically-labeled,etc.) map which prescribes the context of the autonomous agent at anygiven time based on its location and/or orientation (e.g., pose) withinthe map, but can additionally or alternatively include any other map(e.g., map labeled in an automated fashion, map labeled through bothmanual and automated processes, etc.) and/or combination of maps. Inadditional or alternative variations, the map information includes anyor all of: road infrastructure information and/or other staticenvironment information, route information, and/or any other suitableinformation.

In specific examples, the map prescribes one or more contexts (and/ortransition zones) selected based on (e.g., predetermined/assigned to) aregion/location of the autonomous agent (e.g., as determined based onsensor information as described above).

The set of inputs preferably includes sensor information collected at asensor system of the autonomous agent, such as any or all of: a sensorsystem onboard the autonomous agent, a sensor system remote from theautonomous agent, and/or a sensor system in communication with theautonomous agent and/or a computing system (e.g., onboard computingsystem, remote computing system, etc.) of the autonomous agent.Additionally or alternatively, the sensor information can be collectedfrom any other suitable sensor(s) and/or combination of sensors, S205can be performed in absence of collecting sensor inputs, and/or S205 canbe performed in any other suitable way(s).

The sensor information can include and/or be used to determine locationinformation associated with the autonomous agent, such as any or all of:position, orientation (e.g., heading angle), pose, geographical location(e.g., using global positioning system [GPS] coordinates, using othercoordinates, etc.), location within a map, and/or any other suitablelocation information. In preferred variations, for instance, S205includes receiving pose information from a localization module of thesensor subsystem, wherein the localization module includes any or allof: GPS sensors, IMUs, LIDAR sensors, cameras, and/or any other sensors(e.g., as described above). Additionally or alternatively, any othersensor information can be received from any suitable sensors.

The sensor information can additionally or alternatively include and/orbe used to determine motion information and/or other dynamic informationassociated with the autonomous agent, such as, but not limited to, anyor all of: velocity/speed, acceleration, and/or any other suitableinformation.

The sensor information can additionally or alternatively include and/orbe used to determine (e.g., at a perception module) location informationand/or motion information associated with one or more dynamic objects inan environment of the autonomous agent, such as any or all of thelocation information described above, location information relative tothe autonomous agent, motion information of the dynamic objects,predicted information (e.g., predicted trajectory), historicalinformation (e.g., historical trajectory), and/or any other suitableinformation. The dynamic objects can include, but are not limited to,any or all of: other vehicles (e.g., autonomous vehicles, non-autonomousvehicles, 4-wheeled vehicles, 2-wheeled vehicles such as bicycles,etc.), pedestrians (e.g., walking, running, rollerblading,skateboarding, etc.), animals, and/or any other moving objects (e.g.,ball rolling across street, rolling shopping cart, etc.). Additionallyor alternatively, the sensor information can include any otherinformation associated with one or more dynamic objects, such as thesize of the dynamic objects, an identification of the type of object,other suitable information, and/or the information collected in S205 canbe collected in absence of dynamic object information.

The sensor information can additionally or alternatively include and/orbe used to determine (e.g., at a perception module) location informationand/or other information associated with one or more static objects(e.g., stationary pedestrians, road infrastructure, construction siteand/or construction equipment, barricade(s), traffic cone(s), parkedvehicles, etc.) in an environment of the autonomous agent, such as anyor all of the information described above (e.g., identification ofobject type, etc.). Additionally or alternatively, the sensorinformation can include any other information associated with one ormore static objects and/or the information collected in S205 can becollected in absence of static object information.

The set of inputs can include a vehicle context, which specifies anenvironment of the vehicle, and can function to characterize a drivingcontext of the vehicle. The context is preferably prescribed based on afixed route selected for the vehicle, and based on a map (e.g.,high-definition, hand labeled map), such as a map as described aboveand/or any other suitable map(s). The context can additionally oralternatively be determined based on any or all of: sensor informationfrom the sensor system such as the location of the agent, and/or anyother suitable information.

In preferred variations, for instance, the contexts are assigned tolocations and/or regions within the map. Each location and/or region inthe map can be assigned any or all of: a single context; multiplecontexts (e.g., indicating an intersection of multiple routes, wherein asingle context is selected based on additional information such as anyor all of the inputs received in S205, etc.); no context (e.g.,indicating a location and/or region not on a fixed route option for theautonomous agent); and/or any combination of contexts. The particularcontext(s) assigned to the location and/or region are preferablydetermined based on the static environment at that location and/orwithin that region, such as any or all of: features of the roadwaywithin that region (e.g., number of lanes, highway vs. residential road,one-way vs. two-way, dirt and/or gravel vs. asphalt, curvature, shouldervs. no shoulder, etc.); landmarks and/or features within that region(e.g., parking lot, roundabout, etc.); a type of zone associated withthat location and/or region (e.g., school zone, construction zone,hospital zone, residential zone, etc.); a type of dynamic objectsencountered at the location and/or region (e.g., pedestrians, bicycles,vehicles, animals, etc.); traffic parameters associated with thatlocation and/or region (e.g., speed limit, traffic sign types, heightlimits for semi trucks, etc.); and/or any other environmentalinformation.

Additionally or alternatively, the assignment of contexts can take intoaccount a set of fixed routes of the vehicle, wherein the map prescribesa sequential series of contexts which the vehicle encounters along thefixed route, wherein the vehicle's location within the map specifieswhich of these sequential contexts the vehicle is arranged within, andwherein the vehicle switches contexts proximal to (e.g., at) thetransition between contexts.

In some variations, the map includes (e.g., assigns, prescribes, etc.)one or more transition zones which are arranged between differentcontexts, and can indicate, for instance, a change in context (e.g.,along a fixed route, along a dynamically determined route, etc.),thereby enabling a switching of contexts to occur smoothly (e.g., bydefining an action space. Assigning transition zones can function, forinstance, to define an action space subsequently in the method whichsmoothly transitions the vehicle from one context to the next (e.g.,preventing the availability of certain actions, prescribing that theagent maintain his or her lane, preventing a turn, etc.) and/or triggersany other process (e.g., the selection of a new 1^(st) learning module).The transition zones can be any or all of: overlapping with (e.g.,partially overlapping with, fully overlapping with, etc.) one or morecontexts; non-overlapping with one or more contexts; and/or anycombination of overlapping and non-overlapping. Additionally oralternatively, the transition zones can be contexts themselves; themethod can be performed in absence of labeled transition zones (e.g., byanticipating the subsequent context); and/or be otherwise performed.

Examples of contexts can include, but are not limited to, any or all of:a one-lane residential road (e.g., in which the agent cannot changecontexts due to road geometry); a one-lane non-residential road; amulti-lane highway (e.g., in which the agent can learn it is less likelyto see pedestrians); a single lane road in a parking lot; a single laneroad with a yellow boundary on the side; a multi-lane fast moving road;regions connecting to roads (e.g., parking lot, driveway, etc.); and/orany other suitable contexts.

The vehicle context is preferably used in subsequent processes of themethod, further preferably in the selection of a 1^(st) learning module(e.g., as described below), which simplifies and/or specifies theavailable actions to the autonomous agent. Additionally oralternatively, the context can be used to determine a scenario which isused in subsequent processes of the method, wherein the scenariofunctions to further specify the context, such as based on any or all ofthe information described above (e.g., speed limit, sensor informationof objects surrounding vehicle, etc.). Examples of scenarios for a firstcontext of (e.g., a two-way residential road) include, but are notlimited to, any or all of: a right turn opportunity; an addition of aright turn lane; a stop sign; a traffic light; a yield sign; acrosswalk; a speed bump; and/or any other scenarios. Examples ofscenarios for a second context (e.g., a multi-lane highway) include, butare not limited to, any or all of: lane changing; merging; overtaking aslow-moving vehicle; and/or any other scenarios. In some variations, forinstance, the context triggers the selection of a model and/or algorithm(e.g., a highly-tuned, context-aware custom inverse reinforcementlearning (IRL) algorithm), which makes high-level scenario selection andcalls a scenario-specific learning module (e.g., a 1^(st) learningmodule as described below) to select an action of the vehicle.Additionally or alternatively, any other suitable algorithms orprocesses for selecting a scenario can be implemented, an action can beselected in absence of a scenario, a context can be used to selectanother parameter, and/or the method 200 can be otherwise performed.

Additionally or alternatively, the method 200 can include determiningthe vehicle context and/or scenario (e.g., from the map and sensorinformation, from sensor information alone, from other information,etc.) and/or otherwise using a vehicle context, scenario, and/or otherinformation relevant to an environment of the vehicle.

Further additionally or alternatively, any other suitable inputs can bereceived in S205.

In a first set of variations, S205 includes receiving sensor informationfrom a sensor system of the autonomous agent and a labeled mapindicating a set of contexts assigned to a route (e.g., fixed route)and/or a potential route of the agent, wherein a context of the agent isdetermined based on the map and the sensor information. Any or all ofthe set of inputs (e.g., sensor information) are preferably receivedcontinuously throughout the method 200, but can additionally oralternatively be received at any other suitable times.

In a set of specific examples, the sensor information includes at leasta location and/or orientation of the agent (e.g., a pose), information(e.g., location, orientation, motion, etc.) associated with dynamicand/or static objects in an environment of the agent, and optionally anyother information, wherein the context of the agent is determined basedon the location and/or orientation of the agent within the map.

In a second set of variations, additional or alternative to the first,S205 includes receiving sensor information from a sensor system of theautonomous agent and a context of the agent (e.g., a current context, anapproaching context, etc.). The set of inputs are preferably receivedcontinuously throughout the method 200, but can additionally oralternatively be received at any other suitable times.

In a set of specific examples, the context is determined based on a mapand a pose of agent, wherein the context is used subsequently in themethod to select one of a 1^(st) set of learning modules.

4.2 Method—Selecting a Learning Module from a Set of Learning ModulesS210

The method 200 includes selecting a learning module from a set oflearning modules S210, which functions to select a specific (e.g., mostrelevant, optimal, specifically trained, etc.) learned model with whichto determine a set of one or more outputs. S210 further preferablyfunctions to utilize known (e.g., determined, selected, etc.)information associated with the agent (e.g., a selected context, aselected action, etc.) to increase the accuracy and/or confidence of theoutputs of the learning modules. Additionally or alternatively, thelearning modules can function to reduce and/or minimize the number ofavailable outputs to choose from, based on this information, which canconfer these above benefits and/or reduce computing/processing time,and/or perform any other suitable functions.

The selection of learning module is an informed selection of a learningmodule, further preferably a programmed and/or rule-based selection ofwhich of the set of multiple learning modules to implement based oninformation known to the vehicle (e.g., context and/or scenario forselecting a 1^(st) set of learning modules, an action for selecting a2^(nd) set of learning modules, any other environmental feature, sensorinformation, etc.) of the vehicle. Additionally or alternatively,learned processes and/or any other types of determination of a learningmodule can be implemented.

The learning module preferably includes one or more learned modelsand/or algorithms, further preferably a learned model and/or algorithmtrained through one or more machine learning (e.g., deep learning)processes. In preferred variations, each of the learning modulesincludes one or more neural networks (e.g., deep learning network [DNN[,deep Q-learning network, convolutional neural network [CNN]), but canadditionally or alternatively include any other suitable models,algorithms, decision trees, lookup tables, and/or other tools.

S210 is preferably performed with a system 100 as described above,further preferably with an onboard computing system of the autonomousagent, but can additionally or alternatively be performed with any othercomponents of the system 100 and/or any other suitable systems.

S210 can be performed once and/or multiple times throughout the method,such as any or all of: continuously, at a predetermined frequency, at aset of intervals (e.g., random intervals, etc.), in response to a change(e.g., predetermined change) in the set of inputs received in S205(e.g., change in context), in response to an output produced by a priorlearning module (e.g., selecting one of a 2^(nd) set of learning modulesin response to an action and/or action space produced by a 1^(st)learning module), in response to any other suitable trigger(s), and/orat any other suitable times during the method 200.

S210 is preferably performed (e.g., partially performed, fullyperformed, etc.) in response to receiving inputs in S205, but canadditionally or alternatively be performed at any other times and/or inresponse to any other suitable triggers.

In a preferred set of variations, S210 is performed multiple timesthroughout the method (e.g., from context selection to trajectorygeneration), such as described below in S212 and S214, which functionsto increase the explainability and/or interpretability of the method 200(e.g., in comparison to only performing S210 once). In variations inwhich one of a 1^(st) set of learning modules is used to determine anaction and/or action space for the vehicle in light of the vehicle'scontext and one of a 2^(nd) set of learning modules is used to generatea trajectory for the agent based on the action and/or action space, eachof these intermediate outputs maintains explainability andinterpretability. Further, by having these highly focused micro modules,each of the modules can be trained to a highly tuned loss functionspecific to the environment (e.g., context) and/or actions of the agent.Additionally or alternatively, having multiple processes in which alearning module is selected can confer any other suitable benefits.

In an alternative set of variations, S210 is performed once during themethod 200 (e.g., only S212, only S214, in a single learning module fromcontext to trajectory generation, in a single learning module whicheffectively combines the learning modules of S212 and S214, etc.).

Additionally or alternatively, S210 can be performed any number of timesand to produce any suitable outputs during the method 200.

4.3 Method—Selecting a 1^(st) Learning Module S212

S210 preferably includes selecting a 1^(st) learning module S212, whichfunctions to select a learning module tuned to (e.g., trained based on,with a highly tuned loss function corresponding to) the particularenvironment of the agent, further preferably a context (and/or scenario)of the agent. In preferred variations, for instance, S212 functions toselect a particular learned model (e.g., decision network) from a set ofmultiple learned models based on the particular context (e.g., asdescribed above) of the vehicle. S212 can additionally or alternativelyfunction to define an action space available to the agent, inform atrajectory of the agent as determined by a trajectory planner, select alearning module based on other environmental information relative to theagent, select a learning module based on other information relative tothe agent (e.g. historical information, object information, etc.),eliminate available options to the agent (e.g., eliminate availableactions), and/or can perform any other suitable functions.

Selecting a 1^(st) learning module is equivalently described herein asselecting one of a 1^(st) set of learning modules and/or selecting oneof a set of 1^(st) learning modules.

S212 is preferably performed in response to (e.g., after, based on,etc.) S205 (e.g., a most recent instance of S205), but can additionallyor alternatively be performed as part of S214 and/or concurrently withS214, in absence of S214, multiple times throughout the method (e.g., inresponse to the context changing), and/or at any other time(s) duringthe method 200. Further additionally or alternatively, the method 200can be performed in absence of S212.

In some variations, S212 is performed in response to a triggerindicating that a context of the vehicle (e.g., as determined based onits location on a map) has changed and/or is about to change. Thistrigger can be determined based on any or all of: a predicted and/orknown time at which the context will change (e.g., based on the map anda fixed route, based on historical information, etc.); a predictedand/or known distance until a new context (e.g., based on the map and afixed route, based on historical information, etc.); the location of theagent within a transition zone on the map; and/or any other suitableinformation. Additionally or alternatively, S212 can be performed basedon other triggers, continuously and/or at a predetermined frequency, inabsence of a trigger, and/or in any other ways (e.g., as describedabove).

A single learning module from a 1^(st) set of learning modules ispreferably selected based on a context of the vehicle and/or a scenariodetermined based on the context. Additionally or alternatively, theparticular learning module can be determined and/or selected based onother information received in S205 and/or any other suitableinformation. Further additionally or alternatively, multiple learningmodules from the 1^(st) set of learning modules can be selected (e.g.,to be processed in series, to be processed in parallel, etc.).

The learning module is further preferably selected based on a mappingbetween contexts and learning modules. In preferred variations, eachcontext is associated with a single learning module of the 1^(st) set oflearning modules in a 1:1 mapping, wherein each context is onlyassociated with a single 1^(st) learning module and wherein each of the1^(st) learning modules is only associated with a single context. Themappings are preferably predetermined (e.g., programmed, rule-based,etc.), but can additionally or alternatively be dynamically determined.Additionally or alternatively, a single context can be associated withmultiple learning modules, wherein one is selected (e.g., further basedon the set of inputs) and/or the module outputs are aggregated; a modulecan be associated with multiple contexts; and/or any other associationcan be established between contexts and learning modules.

Additionally or alternatively, the learning module can be selected basedon other information (e.g., to further narrow down the selection of alearning module).

The learning module is preferably in the form of and/or includes amachine learning model, further preferably in the form of one or moreneural networks and/or models (e.g., deep Q-learning network,convolutional neural network [CNN], inverse reinforcement learning [IRL]model, reinforcement learning [RL] model, imitation learning [IL] model,etc.) trained for a particular context and/or contexts, but canadditionally or alternatively include any other suitable models,algorithms, decision trees, lookup tables, and/or other tools.

In preferred variations, each of the learning modules is a deep learningnetwork (DNN) (e.g., neural network), further preferably a deepQ-learning network trained using an Inverse Reinforcement learningtechnique and/or process, wherein the number of layers (e.g., hiddenlayers) of the neural network can vary for different contexts and/oractions (e.g., between 3-8 layers, 3 or less layers, 8 or more layers,between 2 and 10 layers, between 1 and 15 layers, etc.). Additionally oralternatively, any other suitable networks, algorithms, and/or modelscan be used in the learning module(s), such as, but not limited to, anyor all of: policy gradient methods, finite state machines [FSMs],probabilistic methods (e.g., Partially Observable Markov DecisionProcess [POMDP]), imitation learning [IL], RL or variations of IRL,and/or any other suitable models and/or networks and/or algorithms. Eachof the learning modules is preferably the same type of neural network(e.g., with different numbers of layers, different weights, etc.) and/oralgorithm and/or model, but can alternatively be different (e.g., havedifferent architectures, different neural network types, etc.).

Each of the learning modules is preferably trained based on dataoccurring within the particular context type or context types associatedwith the learning module and optionally additionally based on dataoccurring within one or more fixed routes which pass through the contextand/or contain regions/paths which are identified as being that context.In some variations, for instance, a single learning module applies to aparticular context type, wherein the single learning module is trainedbased on data from locations which satisfy that context. In othervariations, a single learning module applies to a particular contextwithin a particular route, wherein the single learning module is trainedbased on data associated with that particular context in the particularfixed route. Additionally or alternatively, the learning module(s) canbe trained with any suitable data.

Each of the learning modules is further preferably trained with inversereinforcement learning, which functions to determine a reward functionand/or an optimal driving policy for each of the context-aware learningmodules. The output of this training is further preferably a compactfully-connected network model that represents the reward function and anoptimal policy for each learning module. Additionally or alternatively,the learning modules can be otherwise suitably trained (e.g., withreinforcement learning, etc.) and/or implemented.

In a first variation, S212 includes selecting a context-aware learningmodule (equivalently referred to herein as a context-aware learningagent) based on a determined context of the agent. In specific examples,a single context-aware learning module is assigned to each context. Thecontext-aware learning module is preferably trained with an inversereinforcement learning model, but can additionally or alternatively beotherwise trained (e.g., with supervised learning, with semi-supervisedlearning, with unsupervised learning, etc.).

In a second variation, S212 includes selecting from multiplecontext-aware learning models assigned to and/or available to aparticular context, wherein the particular context-aware learning moduleis selected based on any or all of: machine learning, a decision tree,statistical methods, an algorithm, and/or with any other suitabletool(s).

Additionally or alternatively, any other suitable learning modules canbe selected, used, and/or trained.

4.4. Method—Producing an Output Based on the Learning Module S220

The method 200 includes producing an output based on the learning moduleS220, which functions to produce information with which to performdecision making and/or trajectory generation of the autonomous agent.Additionally or alternatively, the output(s) can be used in any otherprocess of operation of the autonomous agent.

In preferred variations, S220 includes defining an action space and/orselecting an action S222 and generating a trajectory S224, but canadditionally or alternatively include one of S222 and S224, and/or anyother suitable output(s).

4.4 Method—Defining an Action Space and/or Selecting an Action S222

The method 200 preferably includes defining an action space and/orselecting an action S222, which functions to define a set of actions(equivalently referred to herein as behaviors and/or maneuvers)available to the agent in light of the vehicle's context and/orenvironment. Additionally or alternatively, S222 can function tominimize a number of available actions to the agent as informed by thecontext, which functions to simplify the process (e.g., reduce the time,prevent selection of an incompatible action, etc.) required to select anaction for the vehicle. In some variations, for instance, the extrainformation and restriction from the context type can reduce the amountof data that is needed to train the different learning modules andbetter tune the learning module to a specific context to increaseaccuracy and confidence. The method 200 can optionally additionally oralternatively include selecting an action from the action space, whichfunctions to determine a next behavior (e.g., switching and/ortransitioning to a different behavior than current behavior, maintaininga current behavior, etc.) of the vehicle.

S222 is preferably performed in response to (e.g., after, based on,etc.) S212, but can additionally or alternatively be performed inresponse to S210, as part of S212 and/or concurrently with S212, inabsence of S212, in response to S205, multiple times throughout themethod, and/or at any other time(s) during the method 200. Furtheradditionally or alternatively, the method 200 can be performed inabsence of S222 (e.g., in variations in which a single learning moduleis used to determine a trajectory based on context).

S222 preferably includes determining an action space of actionsavailable to the vehicle based on the vehicle context and selecting anaction from the action space, but can additionally or alternativelyinclude determining one of these and/or determining any other suitableoutputs.

S222 is preferably performed with the selected 1^(st) learning moduledescribed above, wherein an action space and/or action is produced as anoutput (e.g., intermediate output, final output, etc.) of the learningmodule; additionally or alternatively, the learning module can produceany other suitable outputs. In preferred variations, a determination ofthe context and processing with a learning module selected for thiscontext allows the action space to be relatively small (e.g., relativeto all available actions). In preferred variations, each of the 1^(st)learning modules includes a set of one or more neural networks and/orother models (e.g., trained using an IRL algorithm and/or process,trained using an RL algorithm and/or process, CNNs, RNNs, etc.), whereinany or all of the neural networks are used to determine an action forthe vehicle.

S222 preferably receives a set of inputs, such as any or all of thosedescribed S205, which are received as inputs to the 1^(st) learningmodule, thereby enabling the learning module to select an optimalaction. The set of inputs is preferably received from and/or determinedbased on one or more sensors of the sensor system, but can additionallyor alternatively be received from any suitable sources. In preferredvariations, the 1^(st) learning module receives as an input informationassociated with a set of detected dynamic objects surrounding the agent(e.g., including the object's current position, size, previous path, andpredicted path into the future). Additionally or alternatively, the1^(st) learning module can be designed to perform self-prediction ofdynamic object motion, which can, for instance, simplify the learningprocess (e.g., in terms of time and/or data required). The set of inputsfurther preferably includes information associated with a set of staticobjects (e.g., current state of the static objects including location);a map (e.g., high-definition, hand labeled map specific a series ofcontexts along a fixed route of the agent); routing information requiredto reach the agent destination; the routing information required toreach the destination; the state of the agent; static and dynamic objectinformation (along with their predicted future paths); and/or any othersuitable information.

The selected 1^(st) learning module preferably receives as an input anenvironmental representation of the surroundings of the agent (e.g., asshown in FIG. 3B). This representation preferably includes not onlyinformation from the current time step but also from previous timesteps, but can additionally or alternatively receive informationassociated with any suitable time(s).

Determining the environmental representation can optionally includedetermining a latent space representation based on any or all of the setof inputs, which functions to distill an extremely high order andcomplex amount of information into a smaller latent space representationprior to presenting an environmental representation as an input to the1^(st) learning module. The latent space representation is preferablydetermined based on static and dynamic object information input into afirst neural network (e.g., CNN) of the 1^(st) learning module, whichproduces as an output a more effective latent space representation,granting order invariance for the inputs of the objects. These inputscan then be combined with other inputs (e.g., HD map, routinginformation, and vehicle state) into a second neural network (e.g., CNN,neural network different than the 1^(st) neural network, same neuralnetwork as the 1^(st) neural network, etc.) that represents the entireinput space as the most effective latent space representation.Additionally or alternatively, the latent space representation can beotherwise determined and/or S222 can be performed in absence of a latentspace representation.

In specific examples (e.g., as shown in FIG. 3C), the learning agenttakes as the following information: the set of detected dynamic objectsincluding their current position, size, previous path, and predictedpath into the future; the set of all static objects and their currentstates; a map (e.g., a high-definition map, a high-definitionhand-labeled map, etc.); routing information required to reach thedestination; the current ego state; and/or any other suitableinformation. A first neural network (e.g., an order independentrepresentation recurrent neural network [RNN], a CNN, etc.) is used tooutput a more effective intermediate latent space representation whichgrants order invariance for the object inputs. This data is thencombined along with the map, routing information, and vehicle state andfed into the latent space network which represents the entire inputspace as the most effective latent space representation. Additionally oralternatively, any or all of this information can be received in absenceof a latent space representation and/or a different latent spacerepresentation, the latent space representation can include any numberof neural networks and/or intermediate neural networks, the latent spacerepresentation can be determined in absence of an intermediate network,any other information can be received, any or all of this informationcan be determined by the learning module (e.g., the predicted path ofdynamic objects), and/or S222 can be otherwise suitably performed.

The method 200 can optionally include training any or all of the 1^(st)set of learning modules. The learning modules are preferably trained ata remote computing system of the system 100, but can additionally oralternatively be trained at any suitable location(s). Each module ispreferably trained based on the full environmental representation aspresented above as input and the correct action at every planning cycle.The training process preferably includes two phases, wherein the 1^(st)phase functions to train the latent space representation networks, whichcan be implemented using a single temporary deep network responsible forclassifying all driving actions regardless of the current context. Inorder to achieve this, this training is done on a complete set of dataavailable in the data set. The 2^(nd) phase uses the latent spacerepresentation learned in the 1^(st) phase to train the deep networks towork on a specific context or action. This can be accomplished by fixingthe weights of the latent space network (e.g., stopping all training forthe network), the weights determined based on a loss function (e.g., ahyper-optimized loss function for a context, a hyper-optimized lossfunction for an action, etc.), thereby removing the temporary deepnetwork, and creating the full set of networks which will be used tomake the final decision. Each of the deep networks is preferably trainedon the subset of the data within the context that it is configured toclassify.

Additionally or alternatively, the 1^(st) learning modules can beotherwise configured and/or trained (e.g., with supervised learning,with semi-supervised learning, with unsupervised learning, etc.).

In a preferred set of variations, The 1^(st) set of learning modules(equivalently referred to herein as deep decision networks (DDNs),learning agents, learned models, etc.) (e.g., as shown in FIG. 3A, asshown in FIG. 3B, as shown in FIG. 4, etc.) use the current context ofthe agent as well as a full representation of the environment around theagent to select an action for the vehicle to take during the currentplanning cycle. Vehicle actions can include, for instance, but are notlimited to, any or all of: stopping behind a vehicle, yielding to avehicle, merging onto a road, and/or any other suitable actions. Adepiction of a set of actions determined for two different contexts canbe seen in FIG. 10.

The actions can include, but are not limited to, any or all of:maintaining a lane, changing lanes, turning (e.g., turning right,turning left, performing a U-turn, etc.), merging, creeping, following avehicle in front of the agent, parking in a lot, pulling over, nudging,passing a vehicle, and/or any other suitable actions such as usualdriving actions for human-operated and/or autonomous vehicles.

Each action is preferably associated with a set of parameters, which aredetermined based on the particular context of the agent and optionallyany other suitable inputs (e.g., sensor information, fixed routeinformation, etc.). The parameter values can be any or all of:predetermined (e.g., assigned values for a particular context),dynamically determined (e.g., with the learning module and based onadditional information such as an environmental representation), anycombination, and/or otherwise determined. This highlights a potentialbenefit of this architecture, which is that it can enable variousparameter values to be associated with an action, wherein the contextspecifies the particular value or range of values, thereby enabling theaction learned for different contexts to be associated with parametervalues optimal to that context. In contrast, in conventional methodswhere the method is entirely programmed, for instance, one would need toeither generalize the parameter (e.g., creep distance) to have an overlyconservative value or program multiple values for different cases; andin methods including only learning based approaches, this would lead toan oversimplification of the action across cases, which could result inunpredictable agent behavior at times (e.g., robotic behavior, theultimate production of an infeasible trajectory, etc.).

In preferred variations, an output layer of each learning module is asoftmax layer where the number of output nodes is the number ofavailable actions. In specific examples, for instance, the softmax layerassigns a confidence to each action in the action space, wherein theaction with the highest confidence is provided as an output of thelearning module. Additionally or alternatively, an action space and/oravailable actions can be determined in any other suitable way(s).

In a specific example, a multi-lane highway context produces, with amulti-lane highway learning module, a corresponding action spaceincluding: maintaining speed, lane change left, and lane change right.In contrast, a different context such as a residential road producesactions such as those in the highway context and additional actions suchas stop, yield, creep, left turn, and right turn.

In additional or alternative variations, an output layer (e.g., linearoutput layer) can be used to generate an embedding (e.g., a vector, avector of real numbers, etc.) for the action, wherein the embeddingcould be matched to stored embeddings associated with particular actions(e.g., at a lookup table). In specific examples, for instance, a lengthand/or angle of an embedding vector produced by an output layer can beused to match it to a vector associated with a particular action.

Selecting an action can be performed by any or all of: the context-awarelearning module, performed with another model and/or algorithm and/orprocess, determined based on other information (e.g., any or all of theset of inputs from S212, based on the particular route, based on a nextcontext in the map, etc.), and/or otherwise determined.

In preferred variations, the action is produced as an output (e.g.,single output, multiple outputs, etc.) of the context-aware learningagent.

In additional or alternative variations, the action can be determinedbased on a state machine or other rule-based method for choosing anaction based on context.

In a first variation, the context of the agent is determined from a mapto be a one-lane residential road (e.g., in which the agent cannotchange contexts due to road geometry as shown in FIG. 10). A set ofactions determined for this context can include, for instance:maintaining speed, creeping, left turning, right turning, and yielding.For creeping, a major parameter is creep distance, which refers to thedistance the agent should creep forward with extra caution (e.g., beforedeciding to merge). For instance, humans tend to creep at a stop sign orbefore merging on a highway to cautiously gauge any oncoming traffic andpace the speed of the vehicle to merge without collisions or annoyanceto road users. Depending on the particular context and optionallyaction, the value of this parameter is different. In specific examples,for the context of a parking lot and the action of turning right and/orstopping at a stop sign, the creep distance is 2 meters, whereas for thecontext of a multi lane highway and the action of merging, the creepdistance is 17 meters.

In a second variation, the context of the agent is determined to be amulti-lane highway in which the agent can learn (e.g., in the learningmodule) it is less likely to see pedestrians. The actions of the actionspace can include, for instance: lane swap left, lane swap right,maintain speed, and stop.

Additionally or alternatively, S222 can include any other suitableprocesses performed in any suitable way(s).

4.5 Method—Selecting a 2^(nd) Learning Module S214

S210 preferably includes selecting a 2^(nd) learning module(equivalently referred to herein as a deep trajectory network) S214,which functions to select a learning module based on the action, whichpreferably additionally functions to select an action-specific modulewith which to determine the agent's trajectory. The 2^(nd) learningmodule is preferably tuned to (e.g., trained based on, with a highlytuned loss function corresponding to) the particular action (and/ormultiple actions in an action space) of the agent. In preferredvariations, for instance, S214 functions to select a particular learnedmodel (e.g., decision network) from a set of multiple learned modelsbased on the particular action (e.g., as described above) selected forthe vehicle (e.g., based on context). S214 can additionally oralternatively function to determine a trajectory of the agent, select alearning module based on other environmental information relative to theagent, select a learning module based on other information relative tothe agent (e.g. historical information, object information, etc.),eliminate available options to the agent (e.g., eliminate availabletrajectories), and/or can perform any other suitable functions.

Selecting a 2^(nd) learning module is equivalently described herein asselecting one of a 2^(nd) set of learning modules and/or selecting oneof a set of 2^(nd) learning modules.

S214 is preferably performed in response to (e.g., after, based on,etc.) S222 (e.g., a most recent instance of S222), wherein S222 ispreferably performed in response to S212, such that the 2^(nd) learningmodule is selected based on an action which is determined based on acontext of the agent. Additionally or alternatively, S214 can beperformed as part of and/or combined with S212, concurrently with S212,in absence of S212, multiple times throughout the method (e.g., inresponse to the context changing), and/or at any other time(s) duringthe method 200. Further additionally or alternatively, the method 200can be performed in absence of S214.

In some variations, S214 is automatically performed in response to S212being performed and an action being determined and/or a triggerindicating that a context of the vehicle (e.g., as determined based onits location on a map) has changed and/or is about to change.Additionally or alternatively, S214 can be performed based on othertriggers, continuously and/or at a predetermined frequency, in absenceof a trigger, and/or in any other ways (e.g., as described above).

A single learning module from the 2^(nd) set of learning modules ispreferably selected based an action selected for the vehicle in S222.Additionally or alternatively, the particular learning module can bedetermined and/or selected based on an action determined in any othersuitable way, the selected 1^(st) learning module, information receivedin S205, and/or any other suitable information. Further additionally oralternatively, multiple learning modules from the 2^(nd) set of learningmodules can be selected (e.g., to be processed in series, to beprocessed in parallel, etc.).

The 2^(nd) learning module is further preferably selected based on amapping between actions and 2^(nd) learning modules. In preferredvariations, each action is associated with a single learning module ofthe 2^(nd) set of learning modules in a 1:1 mapping (e.g., as stored inlookup table and/or database), wherein each action is only associatedwith a single 2^(nd) learning module and wherein each of the 2^(nd)learning modules is only associated with a single action. The mappingsare preferably predetermined (e.g., programmed, rule-based, etc.), butcan additionally or alternatively be dynamically determined.Additionally or alternatively, a single action can be associated withmultiple 2^(nd) learning modules, wherein one of the set of 2^(nd)learning modules is selected (e.g., further based on the set of inputs)and/or the module outputs are aggregated; a module can be associatedwith multiple contexts; and/or any other association can be establishedbetween actions and learning modules.

Additionally or alternatively, the 2^(nd) learning module can beselected based on other information (e.g., to further narrow down theselection of a learning module). In some variations, for instance, each(context, action) pair is associated with a single 2^(nd) learningmodule.

The 2^(nd) learning module (equivalently referred to herein as anaction-aware learning agent, a deep trajectory network [DTN], etc.) ispreferably in the form of and/or includes a machine learning model,further preferably in the form of one or more neural networks (e.g.,deep Q-learning network, convolutional neural network [CNN], etc.)trained for a particular action and/or actions, but can additionally oralternatively include any other suitable models, algorithms, decisiontrees, lookup tables, and/or other tools. The deep trajectory networks(DTN) are preferably selected based on the action selected by the deepdecision network (DDN) and preferably function to generate highlyoptimized safe trajectories with action-driven safety constraints duringthe current planning cycle.

The 2^(nd) set of learning modules are preferably selected, optimized,and safely constrained based on a specific action (e.g., as describedabove). In specific examples, each of the 2^(nd) set of learning modulesuses a localized view around the vehicle (e.g., including informationassociated with only the proximal dynamic and static objects, includinginformation associated with only proximal road features, etc.) toultimately plan a safe, effective and naturalistic trajectory which thevehicle should follow (e.g., as described in S224). This data-drivenmodular approach leads to deterministic models which need exponentiallyless data compared to other conventional architectures.

In preferred variations, each of the 2^(nd) set of learning modules is adeep neural network (DNN) (e.g., neural network), further preferably adeep Q-learning network trained using Inverse Reinforcement learning,wherein the number of layers (e.g., hidden layers) of the neural networkcan vary for different actions (e.g., between 3-8 layers, 3 or lesslayers, 8 or more layers, between 2 and 10 layers, between 1 and 15layers, etc.) and/or based on any other information. Additionally oralternatively, any other suitable networks, algorithms, and/or modelscan be used in the learning module(s), such as any or all of thosedescribed above. Each of the set of multiple 2^(nd) learning modules ispreferably the same type of neural network (e.g., with different numbersof layers) and/or algorithm and/or model, but can alternatively bedifferent (e.g., have different architectures, different neural networktypes, etc.). In a set of specific examples, the 2^(nd) learning moduleshas the same architecture as the 1^(st) set of learning modules. Inalternatively examples, the 1^(st) set and 2^(nd) set of learningmodules have different architectures.

Each of the 2^(nd) learning modules is preferably trained based on dataoccurring within the particular action type associated with the 2^(nd)learning module and optionally additionally based on data occurringwithin any or all of: a route (e.g., fixed route) being traveled by thevehicle, the context of the vehicle, and/or any other suitableinformation. In some variations, for instance, a single 2^(nd) learningmodule applies to a particular action type, wherein the single 2^(nd)learning module is trained based on data wherein the vehicle isperforming the action. Additionally or alternatively, the single 2^(nd)learning module is trained based on data associated with the contextselected prior in S212. Additionally or alternatively, the 2^(nd)learning module(s) can be trained with any suitable data.

Each of the 2^(nd) learning modules is further preferably trained withinverse reinforcement learning, which functions to determine a rewardfunction and/or an optimal driving policy for each of the context-awarelearning modules. The output of this training is further preferably acompact fully-connected network model that represents the rewardfunction and an optimal policy for each learning module. Additionally oralternatively, the learning modules can be otherwise suitably trained(e.g., with reinforcement learning, supervised learning, semi-supervisedlearning, unsupervised learning, etc.) and/or implemented.

In a first variation, S214 includes selecting a 2^(nd) learning module(equivalently referred to herein as an action-aware learning module)based on a determined action of the agent. In specific examples, asingle action-aware learning module is assigned to each action. Theaction-aware learning module is preferably trained with an inversereinforcement learning model, but can additionally or alternatively beotherwise trained.

In a second variation, S214 includes selecting a 2^(nd) learning module(equivalently referred to herein as an action-aware learning module)based on a determined action of the agent along with the context thatled to the action. In specific examples, a single action-aware learningmodule is assigned to each (context, action) pair.

In a third variation, S214 includes selecting from multiple action-awarelearning modules assigned to/available to a particular action, whereinthe particular action-aware learning module is selected based on any orall of: machine learning, a decision tree, statistical methods, analgorithm, and/or with any other suitable tool(s).

Additionally or alternatively, any other suitable learning modules canbe selected, used, and/or trained.

4.6 Method—Generating a Vehicle Trajectory S224

The method preferably includes generating a vehicle trajectory S224,which functions to generate a trajectory for the agent to follow toperform the selected action. Additionally or alternatively, S214 canfunction to generate a most optimal trajectory for the agent (e.g., byeliminating trajectories from consideration based on the action), reducea time and/or processing required to generate a trajectory, and/orperform any other suitable functions.

S224 is preferably performed in response to (e.g., after, based on,etc.) S214, but can additionally or alternatively be performed inresponse to S210 and/or S212, as part of S214 and/or concurrently withS214, in absence of S212 and/or S214, in response to S205, multipletimes throughout the method, and/or at any other time(s) during themethod 200. Further additionally or alternatively, the method 200 can beperformed in absence of S224.

S224 is preferably performed with a selected 2^(nd) learning module asdescribed above, wherein the trajectory is produced as an output of the2^(nd) learning module and/or determined based on an output of the2^(nd) learning module, but can additionally or alternatively beperformed with a 1^(st) learning module, a combined 1^(st) and 2^(nd)learning module, multiple learning modules, any deep learning process,any programmed process, and/or any other suitable processes.

S224 preferably includes determining (e.g., calculating) a safety tunneland a set of safety tunnel constraints associated with the agent, whichdefines a constrained driving region for the autonomous agent based onthe selected action. The safety tunnel is preferably determined based onthe selected action and functions to constrain the set of all availabletrajectories to the agent by sharpening the environment for where thefuture trajectory can be. In some variations, for instance, thisfunctions to limit the environment to only the environment relevant tothe selected action and where the vehicle might be in the future basedon the selected action. The safety tunnel is further preferablycalculated based on a set of inputs including a location of the agent aswell as map information such as: road boundaries, location of stopsigns, location of traffic lights, but can additionally or alternativelytake into account any other suitable inputs. Additionally oralternatively, the safety tunnel can be calculated based on any othersuitable information.

The safety tunnel is preferably a region defined relative to a fixedpoint, plane, and/or surface of the autonomous agent (e.g., front wheel,outermost surface of front bumper, etc.) and/or associated with theautonomous agent (e.g., a virtual point and/or plane and/or surfacerelative to and moving with the autonomous agent) and which extends toany or all of: a predetermined distance (e.g., 100 meters, between 50meters and 100 meters, less than 50 meters, between 100 and 150 meters,150 meters, between 150 meters and 200 meters, greater than 200 meters,etc.), a planning horizon, a stopping object (e.g., yielding sign, stopsign, traffic light, etc.) at which the vehicle must stop, and/or anyother suitable information. The parameters of the safety tunnel arepreferably determined based on the action, such as, but not limited to,any or all of: predetermined assignments, dynamically determinedassignments, an output of the 1^(st) learning module, and/or based onany other information. The safety tunnel is preferably calculated ateach planning cycle (e.g., running 30 times per second, running 10 timesper second, running 50 times per second, running between 0 and 60 timesper second, running greater than 60 times per second, etc.), but canadditionally or alternatively be calculated at any or all of:continuously, at a predetermined frequency, at a set of intervals (e.g.,random intervals), in response to a trigger, and/or at any othersuitable times. The safety tunnel functions to represent all possiblelocations that the agent can occupy for the current selected action. Thesafety tunnel is preferably constrained by the current lane of the agentunless the action identifies a change lane action, but can additionallyor alternatively be otherwise constrained.

In a specific example where the agent is stopped at a stop sign andwhere the possible actions are to continue yielding for other traffic orto merge onto the lane, if the action is to continue yielding forvehicles, the safety tunnel would only extend to the stop sign and notbeyond, limiting the movement of the agent (equivalently referred toherein as an ego vehicle). If the action switches to merge onto thelane, the safety tunnel is programmatically switched to encapsulate thefull space of the lane the agent is meant to merge into.

In another specific example, another vehicle that is 100 meters behindthe ego vehicle on a neighboring lane is not relevant (e.g., outside thesafety tunnel) if the current action is to keep driving straight in thecurrent lane. This, however, becomes relevant (e.g., in the safetytunnel) if the action is instead to perform a lane change action.

Additionally or alternatively, the safety tunnel can be otherwisedesigned and/or implemented; the method can be performed in absence ofthe safety tunnel; and/or the method can be otherwise performed.

The safety tunnel can optionally be used to select which static anddynamic objects are within the safety tunnel, wherein only those objectsare used for consideration and/or further processing (e.g., indetermining the localized environmental representation, in determining alatent space representation, etc.). In some variations, for instance,localized dynamic and static object selectors (e.g., in the computingsystem) select the relevant surrounding objects based on the actionoutput from the 1^(st) learning module, its associated safety tunnel, aswell as any information about these objects such as their location,distance from the ego vehicle, speed, and direction of travel (e.g., todetermine if they will eventually enter the safety tunnel). Additionallyor alternatively, relevant static and dynamic objects can be determinedin absence of and/or independently from a safety tunnel (e.g., justbased on the selected action, based on a predetermined set of actionconstraints, etc.), all static and dynamic objects can be considered,and/or S224 can be otherwise suitably performed.

In a first set of variations of the safety tunnel, the safety tunnel isused as a constraint in trajectory generation, wherein the safety tunnelsharpens (e.g., localizes based on action, constrains based on action,etc.) the environment of the vehicle by incorporating planninginformation such as a future horizon planning lookahead. In specificexamples, the safety tunnel is used in part to generate a latent spacerepresentation used in the final trajectory generation.

S224 preferably includes receiving a set of inputs, such as any or allof those described above, in S205, in S222, and/or any other suitableinputs.

The set of inputs can include any or all of the inputs described above;additional inputs; different inputs; and/or any suitable set orcombination of inputs. In preferred variations, the set of inputsreceived in S224 includes any or all of: dynamic object information(e.g., within the safety tunnel) and their predicted paths; staticobject information (e.g., within the safety tunnel); one or moreuncertainty estimates (e.g., calculated throughout the method,calculated at every 1^(st) learning module, calculated at every 2^(nd)learning module, etc.); a map and/or inputs from the map; the state ofand/or dynamic information associated with the agent; and/or any othersuitable information.

The set of inputs are preferably used to determine a localizedenvironmental representation, which takes into account the informationcollected to determine an environmental representation (e.g., asdescribed previously), along with action-based constraints (e.g., basedon parameters from the safety tunnel and/or the safety tunnelconstraints such as a more limited field of view), thereby producing amore targeted, relevant, and localized environmental representation forthe agent based on the action selected, which is equivalently referredto herein as a localized environmental representation. This canfunction, for instance, to reduce the amount of information that needsto be considered by and/or processed by the 2^(nd) learning module(e.g., for faster processing). Additionally or alternatively, the sameenvironmental representation as described previously, the localizedenvironmental representation can include other information and/or beotherwise constrained, and/or the localized environmental representationcan be otherwise formed.

Determining the localized environmental representation can optionallyinclude determining a latent space representation. The latent spacerepresentation is preferably determined with the same processes and/or asimilar process for determining the latent space representation asdescribed above, but can additionally or alternatively include any othersuitable latent space representation and/or process for determining alatent space representation. Further additionally or alternatively, S214can be performed in absence of a latent space representation.

In a preferred set of variations, the safety tunnel constraints andlocalized dynamic and static objects, the routing information requiredto reach the destination, and the current agent state, are passed to alatent space representation, which reduces the overall size of theenvironmental representation. This latent space representation is thenused by the set of deep trajectory networks, which are optimized andselected based on a single action to create the final trajectory that isproposed for the agent to follow. Additionally or alternatively, theseinputs can be received at the deep trajectory networks in absence of thelatent space representation. In specific examples, using a single deeptrajectory network for each action of the agent allows each network tobe hyper-tuned and optimized in terms of loss function to correctlyoutput an optimal trajectory for each situation.

The method 200 can optionally include training any or all of the 2^(nd)set of learning modules. The learning modules are preferably trained ata remote computing system of the system 100, but can additionally oralternatively be trained at any suitable location(s). The 2^(nd) set oflearning modules can be trained separately/independently from the 1^(st)set of learning modules and with different sets of inputs and outputs,or can additionally or alternatively be trained together (e.g., based onthe same processes, based on the same data, etc.). The 2^(nd) set oflearning modules are preferably trained with the same training processesas described above, but can additionally or alternatively be trainedwith any suitable processes.

In a first variation of training, for instance, each of the 2^(nd)learning modules uses the action from the training data toprogrammatically build action-based constraints. These constraints areused to build the localized environmental representation around thesafety tunnel which is used as an input to the network, wherein the DTNis trained on the trajectory from the training data. While preferablytrained on a different set of inputs and outputs than the 1^(st) set oflearning modules, each of the 2^(nd) set of learning modules ispreferably trained with the 1^(st) and 2^(nd) training phases asdescribed above. In specific examples, for instance, the weights of theloss function take into account the particular action and what needs tobe optimized for it based on the defined safety tunnel. Additionally oralternatively, the 2^(nd) set of learning modules can be otherwisetrained.

Additionally or alternatively, the 2^(nd) learning modules can beotherwise configured and/or trained.

In a first set of variations, S224 includes determining a trajectory forthe agent with a 2^(nd) learning module selected from a set of multiple2^(nd) learning modules in S214, wherein the 2^(nd) learning modulereceives a localized environmental representation as input, wherein thelocalized environmental representation is determined based onaction-specific-based constraints along with a safety tunnel.

In a second set of variations, S224 includes determining an intermediateoutput from a 2^(nd) learning module, wherein the intermediate output isused to determine a trajectory.

Additionally or alternatively, S224 can include any other suitableprocesses and/or be otherwise performed.

4.7 Method—Validating the Vehicle Trajectory S260

The method 200 can optionally include validating the vehicle trajectoryS260, which functions to ensure that the trajectory is safe andeffective (e.g., in reaching the destination) for the agent.

S260 is preferably performed in response to (e.g., after, based on,etc.) S224, but can additionally or alternatively be performed inresponse to any other suitable process, as part of S224 and/orconcurrently with S224, multiple times throughout the method, and/or atany other time(s) during the method 200. Further additionally oralternatively, the method 200 can be performed in absence of S260.

The trajectory is preferably validated based on a programmed set ofrules, which can include any or all of: checking for collisions thatwould or may occur with static and/or dynamic objects (e.g., with alikelihood and/or confidence above a predetermined threshold, with alikelihood and/or confidence above 10%, with a likelihood and/orconfidence between 5% and 100%, with a likelihood and/or confidence of5% or below, with a likelihood and/or confidence between 10% and 30%,with a likelihood and/or confidence between 30% and 50%, with alikelihood and/or confidence between 50% and 70%, with a likelihoodand/or confidence between 70% and 90%, with a likelihood and/orconfidence of at least 90%, etc.); checking if the trajectory followsthe rules of the road (e.g., traffic laws, best practices, roadinfrastructure, etc.); and/or checking for any other suitable rules. Inan event that the generated trajectory is found to violate one or morerules (e.g., single rule, all rule, etc.) and/or an uncertaintyassociated with the trajectory (e.g., uncertainty associated with thedetermination of the trajectory, uncertainty associated with inputs usedto determine the trajectory such as a probability of input data beingout-of-distribution, both, etc.) exceeds a threshold, a backupprogrammed trajectory (e.g., from a fallback motion planner) can beimplemented and/or any other suitable fallback can be implemented.

In preferred variations, for instance, the set of rules includes a firstset of one or more rules which check for collisions with static ordynamic objects that would and/or may occur with the generatedtrajectory and a second set of one or more rules which check if thetrajectory follows the rules of the road.

Additionally or alternatively, validating the trajectory can optionallyinclude checking to see if the agent stays within the safety tunnel usedto determine the trajectory. Additionally or alternatively, validatingthe trajectory can include any other rules.

Further additionally or alternatively, S260 can include any othersuitable processes, S260 can include one or more learned processes, themethod 200 can be performed in absence of S260, and/or S260 can beotherwise suitably performed.

In an event that the generated trajectory does not satisfy one or moreof these rules, the method 200 preferably includes implementing a backupprogrammed trajectory and/or otherwise implementing a fail-safemechanism (e.g., triggering a fallback trajectory planner, repeatingS224, pulling the vehicle over to the side of the road, stopping thevehicle, etc.). Additionally or alternatively, the method 200 cantrigger any other suitable process.

The method 200 can optionally include operating the vehicle according tothe validated vehicle trajectory. Additionally or alternatively, themethod 200 can include operating the vehicle according the trajectorygenerated in S214, determining a set of control commands based on thetrajectory and/or the validated trajectory, operating the vehicle basedon the set of control commands, and/or any other suitable processes.

5. Variations

In a first set of variations, the method 200 includes: receiving a setof inputs including any or all of: agent state and/or dynamicinformation; static object information; dynamic object information(e.g., past trajectory, predicted trajectory, etc.); sensor information;and/or any other suitable information; receiving and/or determining acontext for the vehicle, the context determined based on a locationparameter (e.g., pose) of the agent and a map; selecting a 1^(st)learning module based on the context and with a mapping (e.g., a 1:1mapping); determining an action for the vehicle with the 1^(st) learningmodule, wherein the 1^(st) learning module receives an environmentalrepresentation as input, the environmental representation determinedbased on the set of inputs; selecting a 2^(nd) learning module based onthe action and with a mapping (e.g., a 1:1 mapping); determining avehicle trajectory with the 2^(nd) learning module, wherein the 2^(nd)learning module receives as input a localized environmentalrepresentation; validating the vehicle trajectory based on a set ofrules and/or based on a set of one or more uncertainties associated withthe trajectory; in an event that the vehicle trajectory is not validated(e.g., based on the set of rules, based on uncertainty estimates, etc.),defaulting to a fallback mechanism and/or fallback motion planner; andin an event that the trajectory is validated, operating the vehiclebased on the validated trajectory. Additionally or alternatively, themethod 200 can include determining one or more latent spacerepresentations, determining (e.g., defining) a safety tunnel, trainingany or all of the learning modules, and/or any other processes performedin any suitable order.

In a set of specific examples, the method 200 includes: receiving a setof inputs, wherein the set of inputs includes a high definition, labeled(e.g., hand-labeled, automatically-labeled, etc.) map which prescribesthe context of the autonomous agent at any given time based on itslocation and/or orientation (e.g., pose) within the map, a set ofdetected dynamic objects and associated information (e.g., currentposition, size, previous path, and predicted path into the future), aset of all static objects and their current states, routing informationrequired to reach the destination, the current ego state, and/or anyother suitable information; determining a latent space representationbased on the set of input and determining a full environmentalrepresentation based on the latent space representation; selecting afirst learning module based on the context of the agent, wherein theselected 1^(st) learning module is determined based on a 1:1 mappingfrom the context to 1^(st) learning module, and wherein the 1^(st)learning module includes a deep Q-learning network trained based on aninverse reinforcement learning algorithm; selecting an action for theagent with the 1^(st) learning module and the full environmentalrepresentation; defining a safety tunnel based on the selected action;determining a latent space representation with the set of inputs and thesafety tunnel and determining a localized environmental representationbased on the latent space representation; selecting a 2^(nd) learningmodule based on the action, wherein the selected 2^(nd) learning moduleis determined based on a 1:1 mapping from the action to the 2^(nd)learning module (e.g., in light of the context) and wherein the 2^(nd)learning module includes a deep Q-learning network trained with aninverse reinforcement learning algorithm; generating a trajectory forthe autonomous agent with the 2^(nd) learning module and the localizedenvironmental representation; validating the trajectory with a set ofrules; and if the trajectory is validated, operating the vehicle basedon the trajectory. Additionally or alternatively, the method 200 caninclude any other processes and/or combination of processes.

In a second set of variations, the method 200 includes: receiving a setof inputs including any or all of: agent state and/or dynamicinformation; static object information; dynamic object information(e.g., past trajectory, predicted trajectory, etc.); sensor information;and/or any other suitable information; receiving and/or determining acontext for the vehicle, the context determined based on a locationparameter (e.g., pose) of the agent and a map; selecting a 1^(st)learning module based on the context and with a mapping (e.g., a 1:1mapping); determining a vehicle trajectory with the 1^(st) learningmodule, wherein the 2^(nd) learning module receives as input anenvironmental representation; validating the vehicle trajectory based ona set of rules; in an event that the vehicle trajectory is not validated(e.g., based on the set of rules, based on uncertainty estimates, etc.),defaulting to a fallback mechanism and/or fallback motion planner; andin an event that the trajectory is validated, operating the vehiclebased on the validated trajectory. Additionally or alternatively, anynumber of learning modules can be implemented to generate thetrajectory.

In a third set of variations, the method 200 includes: receiving a setof inputs including any or all of: agent state and/or dynamicinformation; static object information; dynamic object information(e.g., past trajectory, predicted trajectory, etc.); sensor information;and/or any other suitable information; receiving and/or determining acontext for the vehicle, the context determined based on a locationparameter (e.g., pose) of the agent and a map; selecting a 1^(st)learning module based on the context and with a learned model and/oralgorithm and/or decision tree and/or mapping; determining an action forthe vehicle with the 1^(st) learning module, wherein the 1^(st) learningmodule receives an environmental representation as input, theenvironmental representation determined based on the set of inputs;selecting a 2^(nd) learning module based on the action and with alearned model and/or algorithm and/or decision tree and/or mapping;determining a vehicle trajectory with the 2^(nd) learning module,wherein the 2^(nd) learning module receives as input a localizedenvironmental representation; validating the vehicle trajectory based ona set of rules; in an event that the vehicle trajectory is not validated(e.g., based on the set of rules, based on uncertainty estimates, etc.),defaulting to a fallback mechanism and/or fallback motion planner; andin an event that the trajectory is validated, operating the vehiclebased on the validated trajectory. Additionally or alternatively, themethod 200 can include determining one or more latent spacerepresentations, determining a safety tunnel, training any or all of thelearning modules, and/or any other processes performed in any suitableorder.

Additionally or alternatively, the method 200 can include any othersuitable processes performed in any suitable order.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A system for an autonomous agent, comprising: a set ofmultiple learned models; a computing system configured to receive a setof inputs; and a computer readable medium having stored thereon softwareinstructions that, when executed by the computing system, cause thecomputing system to execute a sequence comprising: selecting a firstlearned model from the set of multiple learned models based on the setof inputs; generating a first output using the first learned model;based on the output of the first learned model, selecting a second modelfrom the set of multiple learned models; and determining a trajectoryfor the autonomous agent based on the second learned model.
 2. Thesystem of claim 1, wherein the first and second learned models areselected from a first and second subset of the set of learned models,respectively, wherein the first and second subsets are disjoint.
 3. Thesystem of claim 2, wherein the first output comprises an action, whereinthe action is associated with a plurality of learned models of the firstsubset.
 4. The system of claim 1, further comprising a sensor systemcommunicatively coupled to the computing system, wherein the set ofinputs comprises sensor data received from the sensor system.
 5. Thesystem of claim 1, wherein the sequence further comprises: validatingthe trajectory with a set of rules.
 6. The system of claim 5, whereinthe trajectory is validated based on an uncertainty value of thetrajectory falling below an uncertainty threshold.
 7. The system ofclaim 5, wherein the set of rules comprise collision avoidanceheuristics.
 8. The system of claim 5, wherein validating the trajectorywith the set of rules comprises validating satisfaction of a trafficlaw.
 9. The system of claim 1, wherein the set of inputs comprises acontextual input.
 10. The system of claim 9, wherein the contextualinput is selected from a set of predetermined context assignments. 11.The system of claim 10, wherein the first model and the context areassociated with each other in a one-to-one mapping.
 12. The system ofclaim 1, wherein the set of inputs comprises a state estimate of theautonomous agent.
 13. The system of claim 1, wherein each of the learnedmodels is a deep Q-learning network previously trained by reinforcementlearning.
 14. The system of claim 1, wherein the sequence furthercomprises facilitating operation of the autonomous agent based on thetrajectory.
 15. The system of claim 1, wherein, when executed by thecomputing system, the software instructions cause the computing systemto repeatedly execute the sequence.
 16. A computer readable mediumhaving stored thereon software instructions that, when executed by aprocessing system of an autonomous agent, cause the processing system todetermine a trajectory for the autonomous agent by: determining acontext of the autonomous agent from a map of labeled contexts;selecting a first learned model from a first plurality of learned modelsbased on the context; determining a first output of the first learnedmodel; based on the first output, selecting a second learned model froma second plurality of learned models; and determining a trajectory forthe autonomous agent based on the second learned model.
 17. The computerreadable medium of claim 16, wherein the second learned model isselected based on a stored mapping of the first output to the secondmodel.
 18. The computer readable medium of claim 16, wherein the firstand second plurality of learned models are distinct.
 19. The computerreadable medium of claim 16, wherein each of the learned models of thefirst and second plurality is a deep Q-learning network previouslytrained by reinforcement learning.
 20. The computer readable medium ofclaim 19, wherein the learned models of the first plurality are trainedwith disjoint sets of training data.